This is the R code used to produce the map of the LOTVS database

The LOTVS (LOng-Term Vegetation Sampling) database is a collection of vegetation data using permanent plots worldwide. At present the database includes vegetation data from 80 study areas covering grasslands, shrublands and forest understory around the globe, with more that 8000 plots and data for ~4500 vascular plant species, sampled for at least for 6 years (with a range of 6 to 53 mostly on yearly basis).

library(leaflet)
library(leaflet.esri)
## Loading required package: leaflet.extras
library(leafem)
library(rmapshaper)
## Registered S3 method overwritten by 'geojsonlint':
##   method         from 
##   print.location dplyr
library(htmltools)
library(htmlwidgets)
library(sf)
## Linking to GEOS 3.8.0, GDAL 3.0.4, PROJ 6.3.1
#Load LOTVS points
LOTVS_pts <- read.csv(file = "D:/WorldEcologicalLandUnits/LOTVS/Trial.csv", header = T, dec = ",", sep = ";")
#Transform to spatial object
LOTVS_spatial <- st_as_sf(x = LOTVS_pts, coords = c("long", "lat"))
#Load biomes polygons
Biomes <- st_read("D:/WorldEcologicalLandUnits/TerEcorTNC/Diss_fx_tnc_ecor.shp")
## Reading layer `Diss_fx_tnc_ecor' from data source `D:\WorldEcologicalLandUnits\TerEcorTNC\Diss_fx_tnc_ecor.shp' using driver `ESRI Shapefile'
## Simple feature collection with 16 features and 16 fields
## geometry type:  MULTIPOLYGON
## dimension:      XY
## bbox:           xmin: -180 ymin: -89.9 xmax: 180 ymax: 83.6236
## geographic CRS: WGS 84
#Set CRS
st_crs(LOTVS_spatial) <- st_crs(Biomes)

#Select only some cols of biomes
Biomes <- Biomes[c("WWF_REALM", "WWF_MHTNAM")]

#Simplify biomes
Biomes_simplified <- rmapshaper::ms_simplify(input = Biomes)

#Remove Antarctic - need tmap for that
library(tmap)
## Warning: package 'tmap' was built under R version 3.6.3
data("World")
World_WGS <- st_transform(World, crs = 4326)
Biomes_simplified <- st_crop(Biomes_simplified, st_bbox(World_WGS[World_WGS$continent != "Antarctica", ])) #check warning
## although coordinates are longitude/latitude, st_intersection assumes that they are planar
## Warning: attribute variables are assumed to be spatially constant
## throughout all geometries
#compare sizes pre vs pst simplify
object.size(Biomes); object.size(Biomes_simplified)
## 46907480 bytes
## 3674784 bytes
#Colours for biomes
palette_biomes <- c("Boreal Forests/Taiga" = "darkslategray3",
                    "Deserts and Xeric Shrublands" = "darkgoldenrod1",
                    "Flooded Grasslands and Savannas" = "darkorange",
                    "Inland Water" = "cyan",
                    "Mangroves" = "deeppink2",
                    "Mediterranean Forests, Woodlands and Scrub" = "darkorange3",
                    "Montane Grasslands and Shrublands" = "yellow",
                    "Rock and Ice" = "cornsilk",
                    "Temperate Broadleaf and Mixed Forests" = "darkolivegreen1",
                    "Temperate Conifer Forests" = "deepskyblue4",
                    "Temperate Grasslands, Savannas and Shrublands" = "darkkhaki",
                    "Tropical and Subtropical Coniferous Forests" = "burlywood3",
                    "Tropical and Subtropical Dry Broadleaf Forests" = "chartreuse4", #check here
                    "Tropical and Subtropical Grasslands, Savannas and Shrublands" = "darkorange4",
                    "Tropical and Subtropical Moist Broadleaf Forests" = "darkgreen",
                    "Tundra" = "azure")

palette_biomes.col <- unname(sapply(as.character(Biomes_simplified$WWF_MHTNAM), function(i, colori = palette_biomes) {
  palette_biomes[i]
}))

#Create color palette for categorical data
colors_biomes <- colorFactor(palette = palette_biomes.col, levels = Biomes_simplified$WWF_MHTNAM)

#make LOTVS icon
lotvsIcon <- makeIcon(
  iconUrl = "C:/Users/Manuele/Desktop/LOTVS_off.png",
  iconWidth = 50, iconHeight = 50,
  iconAnchorX = 0, iconAnchorY = 0
)

#multiple layers can be assigned to the same group
#layerID are unique
LOTVS_database <- leaflet(data = Biomes_simplified) %>%
  addEsriBasemapLayer(esriBasemapLayers$Imagery, group = "ESRI-Imagery", autoLabels = F) %>%
  addEsriBasemapLayer(esriBasemapLayers$Streets, group = "ESRI-Streets") %>%
  addPolygons(color = ~colors_biomes(WWF_MHTNAM), stroke = FALSE,
              fillOpacity = 0.8, smoothFactor = 0.5, fillColor = 0,
              group = "Biomes") %>%
  addLegend(position = "bottomleft", pal = colors_biomes,
            val = Biomes_simplified$WWF_MHTNAM,
            title = "Biomes", opacity = 0.5) %>%
  addMarkers(lng = st_coordinates(LOTVS_spatial)[, 1], lat = st_coordinates(LOTVS_spatial)[, 2],
             clusterOptions = markerClusterOptions(),
             icon = lotvsIcon, group = "Dataset") %>%
  addLayersControl(baseGroups = c("ESRI-Imagery", "ESRI-Streets"),
                   overlayGroups = c("Biomes", "Dataset"),
                   position = "topright") %>%
  setView(lat = 39.466667, lng = -0.375000, zoom = 1) %>%
  leaflet.extras::addResetMapButton() %>%
  addMiniMap(width = 100, height = 80) %>%
  addLogo(img = "C:/Users/Manuele/Desktop/LOTVS_full.png",
          src = "local", url = "https://lotvs.enraizandolanube.com/",
          position = "topleft", height = 100, width = 300)
#setMaxBounds(lng1 = -180.00000, lat1 = -55.61183, lng2 = 180.00000, lat2 = 83.62302) --> put it if you can manage to reduce the legend

#show map
LOTVS_database
#save map
#save_html(LOTVS_database, "LOTVS.html")

#saveWidget(LOTVS_database, "LOTVS_wdgt.html")